Effective tribological performance-oriented concentration optimization of lubricant additives based on a machine learning approach

Abstract

The tribological performance of lubricant is significantly affected by additive concentration. To realize optimization of additive concentrations, an eXtreme Gradient Boosting machine learning method was proposed to predict the tribological performance of a lubricant, reflected by wear volume, with data collected from four-ball friction experiments. The Shapley Additive exPlanation tool was used to explain the predictions of the model. Particle swarm optimization was used to optimize additive concentration proportions. The tribological properties of grease additives were verified through validation experiment. The chemical components of wear scars include MXenes, carbon films, iron oxides, and iron phosphides, which facilitate direct contact between friction surfaces. This study provides a novel data-driven approach for optimization of additive concentrations with effective and predictable tribological performance.